Container Manager Mcp

CLI or API | MCP | Agent

Version: 2.1.0

Documentation — Installation, deployment, usage across the API, CLI, MCP, and A2A agent interfaces, and the multi-host control plane are maintained in the official documentation.

Overview

Container Manager Mcp is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Container Manager - manage Docker, Docker Swarm, Podman, and Kubernetes containers and workloads. MCP+A2A Servers Out of the Box!.

Full coverage across all three engines: Docker (incl. Swarm/service/stack/config/secret/node operations via cm_docker_swarm), Podman (incl. pod/kube operations, generate/play kube, checkpoint/restore via cm_podman), and a complete Kubernetes surface (RKE2 / k3s / vanilla) spanning workloads, config, networking, storage, RBAC, cluster admin, governance, and observability through 8 themed cm_k8s_* tools built on the official kubernetes Python client. A cm_multi_context tool lets an agent fan out operations across several Docker/Podman/Swarm/Kubernetes contexts in parallel, and cm_ingest_inventory feeds Docker, Swarm, and Kubernetes resources into the ontology-driven Knowledge Graph as typed nodes.

Key Features

• Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.
• Full Docker + Swarm + Podman + Kubernetes Coverage: First-class support for Docker (incl. Swarm/service/stack/config/secret/node ops), rootless Podman (incl. pods, generate/play kube, checkpoint/restore), and a full Kubernetes surface (RKE2 / k3s / vanilla) across workloads, config, networking, storage, RBAC, cluster admin, governance, and observability. See Kubernetes below.
• Multi-Context Parallel Operation: cm_multi_context fans operations out across several Docker, Podman, Swarm, and/or Kubernetes contexts at once (ThreadPoolExecutor-backed), with health checks and lazy reconnect.
• Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
• Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
• Ontology-Driven KG Ingestion: cm_ingest_inventory maps live Docker/Swarm/Kubernetes inventory (containers, images, volumes, networks, services, nodes, pods, deployments, namespaces, native k8s Services) into typed OWL/RDF nodes for cross-source reasoning.
• Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing.

Multi-Host & Zero-Script Remote Docker Orchestration

container-manager-mcp allows a single master instance of the MCP server on your controller to route container and volume operations securely to remote hosts over SSH standard tunneling.

• Unified Inventory: Connection endpoints are loaded dynamically from the XDG shared inventory at ~/.config/agent-utilities/inventory.yml (.yml preferred; a legacy inventory.yaml is still read when no .yml exists).
• Zero TCP Socket Exposure: Operations route directly over the standard SSH channel securely, removing the need to expose Docker socket TCP ports.

Shared inventory: the cm_* host aliases you pass as host come from the same inventory.yml used by tunnel-manager — define your fleet once. Create and validate it with tunnel-manager inventory init / tunnel-manager inventory doctor. See tunnel-manager's Inventory guide for the full schema, template, and override options.

To configure and utilize the multi-host remote routing, see the detailed Multi-Host Architecture Guide.

CLI or API

This agent wraps the Container Manager - manage Docker, Docker Swarm, and Podman containers. MCP+A2A Servers Out of the Box! API. You can interact with it programmatically or via its integrated execution entrypoints.

Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

Environment doctor

Not sure your environment is wired up? Run the guided doctor first. It probes every surface with real checks — python client libs + CLIs, CONTAINER_MANAGER_TYPE / toggles / K8S_CONTEXTS parsing, the tunnel-manager SSH inventory (and per-host reachability), the docker / podman daemons, and each kubernetes context — and prints concrete remediation for anything that is not OK, so you are walked through connecting to your environments. Available as the container-manager-doctor CLI and the cm_doctor MCP tool.

container-manager-doctor                       # diagnose everything (exit 1 if any FAIL)
container-manager-doctor --guided              # + probe every inventory host
container-manager-doctor --backend inventory --host <alias>
container-manager-doctor --backend kubernetes --context <ctx>
container-manager-doctor --backend docker --host <alias>
container-manager-doctor --json                # machine-readable report

The cm_doctor MCP tool mirrors it: action=run for a full sweep, or a focused check_backends / check_inventory / check_docker / check_podman / check_kubernetes. Each check returns {name, category, status: ok|warn|fail, detail, remediation} plus a summary. Start here, then follow the container-manager-config-walkthrough skill.

MCP

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

Auto-generated — do not edit (synced by the mcp-readme-table pre-commit hook).

Condensed action-routed tools (default — MCP_TOOL_MODE=condensed)

Verbose 1:1 API-mapped tools (MCP_TOOL_MODE=verbose or both)

23 action-routed tool(s) (default) · 291 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (condensed default · verbose 1:1 · both). Auto-generated — do not edit.

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/mcp.md.

Kubernetes

container-manager-mcp ships full Kubernetes coverage (RKE2 / k3s / vanilla) through the official kubernetes Python client, exposed as 8 themed, action-routed MCP tools (replacing an earlier, messier tool sprawl):

Two details worth knowing:

• patch_resource (on cm_k8s_config) is a generic patch action supporting strategic / merge / json patch types against any resource kind — use it when there's no dedicated action for a field-level update.
• True core Services vs. Swarm-parity services — cm_k8s_networking's list_k8s_services / get_k8s_service / create_k8s_service / delete_k8s_service operate on real Kubernetes Service objects. This is distinct from cm_multi_context's Swarm-parity list_services, which is Deployment-shaped for cross-backend comparability.

Kubernetes access is configured via CONTAINER_MANAGER_TYPE=kubernetes and CONTAINER_MANAGER_KUBECONTEXT (see Environment Variables); for operating several clusters at once, see K8S_CONTEXTS / cm_multi_context below. For a full worked walkthrough of the tool surface, see the container-manager-kubernetes-operations skill and docs/usage.md.

Multi-Context Operation

cm_multi_context (toggle MULTICONTEXTTOOL) lets a single MCP call operate across several Docker, Podman, Swarm, and/or Kubernetes contexts at once — configured via K8S_CONTEXTS, DOCKER_CONTEXTS, SWARM_CONTEXTS, and their DEFAULT_*_CONTEXT defaults (MULTI_CONTEXT_MODE=True routes every call through it). Fan-out is parallel (ThreadPoolExecutor-backed) with per-backend health checks and lazy reconnect, so an agent can compare, migrate between, or simultaneously act on multiple clusters/engines/hosts in one call. See the container-manager-multi-context skill and docs/usage.md for examples.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
• Environment Variables: Define standard environment variables:
  • MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
  • MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
• HTTP SSE Request Headers: Pass custom headers during transport initialization:
  • x-mcp-enabled-tools / x-mcp-disabled-tools
  • x-mcp-enabled-tags / x-mcp-disabled-tags
• HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

MCP Configuration Examples

Install the slim [mcp] extra. All examples install container-manager-mcp[mcp] — the MCP-server extra that pulls only the FastMCP / FastAPI tooling (agent-utilities[mcp]). It deliberately excludes the heavy agent runtime (pydantic-ai, the epistemic-graph engine, dspy, llama-index), so uvx / container installs are far smaller. Use the full [agent] extra only when you need the integrated Pydantic AI agent.

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

{
  "mcpServers": {
    "container-manager-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "container-manager-mcp[mcp]",
        "container-manager-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "condensed",
        "COMPOSETOOL": "True",
        "CONTAINERTOOL": "True",
        "CONTAINER_MANAGER_HOST": "",
        "CONTAINER_MANAGER_KUBECONTEXT": "",
        "CONTAINER_MANAGER_PODMAN_BASE_URL": "",
        "CONTAINER_MANAGER_TYPE": "docker",
        "DEFAULT_DOCKER_CONTEXT": "",
        "DEFAULT_K8S_CONTEXT": "",
        "DEFAULT_SWARM_CONTEXT": "",
        "DOCKERSWARMTOOL": "True",
        "DOCKER_CONTEXTS": "",
        "DOCTORTOOL": "True",
        "HEALTH_CHECK_TTL_SECONDS": "30",
        "IMAGETOOL": "True",
        "INFOTOOL": "True",
        "INVENTORYTOOL": "True",
        "K8SCLUSTERTOOL": "True",
        "K8SCONFIGTOOL": "True",
        "K8SGOVERNANCETOOL": "True",
        "K8SNETWORKINGTOOL": "True",
        "K8SOBSERVABILITYTOOL": "True",
        "K8SRBACTOOL": "True",
        "K8SSTORAGETOOL": "True",
        "K8SWORKLOADSTOOL": "True",
        "K8S_CONTEXTS": "",
        "KUBECONFIG": "",
        "KUBERNETES_SERVICE_HOST": "",
        "MISCTOOL": "True",
        "MULTICONTEXTTOOL": "True",
        "MULTI_CONTEXT_MAX_WORKERS": "",
        "MULTI_CONTEXT_MODE": "True",
        "NETWORKTOOL": "True",
        "PODMANTOOL": "True",
        "PODMAN_ENABLED": "true",
        "SPECIALIST_DEPLOYMENTTOOL": "True",
        "SWARMTOOL": "True",
        "SWARM_CONTEXTS": "",
        "SYSTEMTOOL": "True",
        "VOLUMETOOL": "True"
      }
    }
  }
}

Streamable-HTTP Transport (networked / production)

{
  "mcpServers": {
    "container-manager-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "container-manager-mcp[mcp]",
        "container-manager-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "MCP_TOOL_MODE": "condensed",
        "COMPOSETOOL": "True",
        "CONTAINERTOOL": "True",
        "CONTAINER_MANAGER_HOST": "",
        "CONTAINER_MANAGER_KUBECONTEXT": "",
        "CONTAINER_MANAGER_PODMAN_BASE_URL": "",
        "CONTAINER_MANAGER_TYPE": "docker",
        "DEFAULT_DOCKER_CONTEXT": "",
        "DEFAULT_K8S_CONTEXT": "",
        "DEFAULT_SWARM_CONTEXT": "",
        "DOCKERSWARMTOOL": "True",
        "DOCKER_CONTEXTS": "",
        "DOCTORTOOL": "True",
        "HEALTH_CHECK_TTL_SECONDS": "30",
        "IMAGETOOL": "True",
        "INFOTOOL": "True",
        "INVENTORYTOOL": "True",
        "K8SCLUSTERTOOL": "True",
        "K8SCONFIGTOOL": "True",
        "K8SGOVERNANCETOOL": "True",
        "K8SNETWORKINGTOOL": "True",
        "K8SOBSERVABILITYTOOL": "True",
        "K8SRBACTOOL": "True",
        "K8SSTORAGETOOL": "True",
        "K8SWORKLOADSTOOL": "True",
        "K8S_CONTEXTS": "",
        "KUBECONFIG": "",
        "KUBERNETES_SERVICE_HOST": "",
        "MISCTOOL": "True",
        "MULTICONTEXTTOOL": "True",
        "MULTI_CONTEXT_MAX_WORKERS": "",
        "MULTI_CONTEXT_MODE": "True",
        "NETWORKTOOL": "True",
        "PODMANTOOL": "True",
        "PODMAN_ENABLED": "true",
        "SPECIALIST_DEPLOYMENTTOOL": "True",
        "SWARMTOOL": "True",
        "SWARM_CONTEXTS": "",
        "SYSTEMTOOL": "True",
        "VOLUMETOOL": "True"
      }
    }
  }
}

Alternatively, connect to a pre-deployed Streamable-HTTP instance by url:

{
  "mcpServers": {
    "container-manager-mcp": {
      "url": "http://localhost:8000/container-manager-mcp/mcp"
    }
  }
}

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name container-manager-mcp-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e HOST=0.0.0.0 \
  -e PORT=8000 \
  -e MCP_TOOL_MODE=condensed \
  -e COMPOSETOOL=True \
  -e CONTAINERTOOL=True \
  -e CONTAINER_MANAGER_HOST="" \
  -e CONTAINER_MANAGER_KUBECONTEXT="" \
  -e CONTAINER_MANAGER_PODMAN_BASE_URL="" \
  -e CONTAINER_MANAGER_TYPE=docker \
  -e DEFAULT_DOCKER_CONTEXT="" \
  -e DEFAULT_K8S_CONTEXT="" \
  -e DEFAULT_SWARM_CONTEXT="" \
  -e DOCKERSWARMTOOL=True \
  -e DOCKER_CONTEXTS="" \
  -e DOCTORTOOL=True \
  -e HEALTH_CHECK_TTL_SECONDS=30 \
  -e IMAGETOOL=True \
  -e INFOTOOL=True \
  -e INVENTORYTOOL=True \
  -e K8SCLUSTERTOOL=True \
  -e K8SCONFIGTOOL=True \
  -e K8SGOVERNANCETOOL=True \
  -e K8SNETWORKINGTOOL=True \
  -e K8SOBSERVABILITYTOOL=True \
  -e K8SRBACTOOL=True \
  -e K8SSTORAGETOOL=True \
  -e K8SWORKLOADSTOOL=True \
  -e K8S_CONTEXTS="" \
  -e KUBECONFIG="" \
  -e KUBERNETES_SERVICE_HOST="" \
  -e MISCTOOL=True \
  -e MULTICONTEXTTOOL=True \
  -e MULTI_CONTEXT_MAX_WORKERS="" \
  -e MULTI_CONTEXT_MODE=True \
  -e NETWORKTOOL=True \
  -e PODMANTOOL=True \
  -e PODMAN_ENABLED=true \
  -e SPECIALIST_DEPLOYMENTTOOL=True \
  -e SWARMTOOL=True \
  -e SWARM_CONTEXTS="" \
  -e SYSTEMTOOL=True \
  -e VOLUMETOOL=True \
  knucklessg1/container-manager-mcp:mcp

Auto-generated from the code-read env surface (MCP_TOOL_MODE + package vars) — do not edit.

Additional Deployment Options

container-manager-mcp can also run as a local container (Docker / Podman / uv) or be consumed from a remote deployment. The Deployment guide has full, copy-paste mcp_config.json for all four transports — stdio, streamable-http, local container / uv, and remote URL:

• Local container / uv — launch the server from mcp_config.json via uvx, docker run, or podman run, or point at a local streamable-http container by url.
• Remote URL — connect to a server deployed behind Caddy at http://container-manager-mcp.arpa/mcp using the "url" key.

Environment Variables

Package environment variables

Inherited agent-utilities variables (apply to every connector)

50 package + 14 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit.

Every variable is listed in the auto-generated table above (package vars from .env.example + the inherited agent-utilities surface). A few pointers:

• Tool toggles — each action-routed tool can be disabled via its <DOMAIN>TOOL toggle; the tool ↔ toggle mapping is in the Available MCP Tools table above.
• Multi-host control plane — remote host endpoints load from the XDG shared inventory ~/.config/agent-utilities/inventory.yml (.yml preferred, .yaml legacy fallback), managed via tunnel-manager inventory init|doctor (see Multi-Host guide).

See .env.example for a copy-paste starting point.

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Run the agent server
container-manager-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The following docker/agent.compose.yml configures the Agent, Web UI, and Terminal Interface together:

version: '3.8'

services:
  container-manager-mcp-mcp:
    image: knucklessg1/container-manager-mcp:mcp
    container_name: container-manager-mcp-mcp
    hostname: container-manager-mcp-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

  container-manager-mcp-agent:
    image: knucklessg1/container-manager-mcp:latest
    container_name: container-manager-mcp-agent
    hostname: container-manager-mcp-agent
    restart: always
    depends_on:
      - container-manager-mcp-mcp
    env_file:
      - ../.env
    command: [ "container-manager-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9019
      - MCP_URL=http://container-manager-mcp-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9019:9019"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9019/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/agent.md.

Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

• Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.
• OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
• Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

Installation

Pick the extra that matches what you want to run:

# MCP server only (recommended for tool hosting — slim deps)
uv pip install "container-manager-mcp[mcp]"

# Full agent runtime (Pydantic AI + epistemic-graph engine)
uv pip install "container-manager-mcp[agent]"

# Everything (development)
uv pip install "container-manager-mcp[all]"      # or: python -m pip install "container-manager-mcp[all]"

Container images (:mcp vs :agent)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

docker build --target mcp   -t knucklessg1/container-manager-mcp:mcp    docker/   # slim MCP server
docker build --target agent -t knucklessg1/container-manager-mcp:latest docker/   # full agent

docker/mcp.compose.yml runs the slim :mcp server; docker/agent.compose.yml runs the agent (:latest) with a co-located :mcp sidecar.

Knowledge-graph database (epistemic-graph)

The full agent ([agent] / :latest) embeds the epistemic-graph engine (pulled in transitively via agent-utilities[agent]). For production — or to share one knowledge graph across multiple agents — run epistemic-graph as its own database container and point the agent at it instead of embedding it. Deployment recipes (single-node + Raft HA), connection config, and the full database architecture (with diagrams) are documented in the epistemic-graph deployment guide. The slim [mcp] server does not require the database.

Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

AGENTS.md is the canonical contributor/agent guidance.

Skills

Alongside the MCP tools, container_manager_mcp/skills/ ships Universal Skills that guide an agent through common operational flows:

container-manager-kubernetes-operations, container-manager-podman-operations, container-manager-multi-context, and container-manager-config-walkthrough are new; lifecycle, swarm, and kg-ingestion were updated for the expanded tool surface.

Repository Owners

Contribute

Contributions are welcome! Please ensure code quality by executing local checks before submitting pull requests:

• Format code using ruff format .
• Lint code using ruff check .
• Validate type-safety with mypy .
• Execute test suites using pytest

Deploy with agent-os-genesis

This package can be provisioned for you — skill-guided — by the agent-os-genesis universal skill (its single-package deploy mode): it picks your install method, seeds secrets to OpenBao/Vault (or .env), trusts your enterprise CA, registers the MCP server, and verifies it — the same machinery that stands up the whole Agent OS, narrowed to just this package. Ask your agent to "deploy container-manager-mcp with agent-os-genesis".

Secrets are read-existing + seeded via vault_sync — you are only prompted for what's missing.